Least-Ambiguous Multi-Label Classifier
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914035746734080 |
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| author | Hagos, Misgina Tsighe Lundström, Claes |
| author_facet | Hagos, Misgina Tsighe Lundström, Claes |
| contents | Multi-label learning often requires identifying all relevant labels for training instances, but collecting full label annotations is costly and labor-intensive. In many datasets, only a single positive label is annotated per training instance, despite the presence of multiple relevant labels. This setting, known as single-positive multi-label learning (SPMLL), presents a significant challenge due to its extreme form of partial supervision. We propose a model-agnostic approach to SPMLL that draws on conformal prediction to produce calibrated set-valued outputs, enabling reliable multi-label predictions at test time. Our method bridges the supervision gap between single-label training and multi-label evaluation without relying on label distribution assumptions. We evaluate our approach on 12 benchmark datasets, demonstrating consistent improvements over existing baselines and practical applicability. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_10689 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Least-Ambiguous Multi-Label Classifier Hagos, Misgina Tsighe Lundström, Claes Machine Learning Multi-label learning often requires identifying all relevant labels for training instances, but collecting full label annotations is costly and labor-intensive. In many datasets, only a single positive label is annotated per training instance, despite the presence of multiple relevant labels. This setting, known as single-positive multi-label learning (SPMLL), presents a significant challenge due to its extreme form of partial supervision. We propose a model-agnostic approach to SPMLL that draws on conformal prediction to produce calibrated set-valued outputs, enabling reliable multi-label predictions at test time. Our method bridges the supervision gap between single-label training and multi-label evaluation without relying on label distribution assumptions. We evaluate our approach on 12 benchmark datasets, demonstrating consistent improvements over existing baselines and practical applicability. |
| title | Least-Ambiguous Multi-Label Classifier |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.10689 |